Data trends at meteoLCD: 1998 to 2020
Trends computed from yearly averages at meteoLCD,
Diekirch, Luxembourg.
Graphs may be freely copied and used, under the condition to cite:
MASSEN, Francis: Data trends at meteoLCD, 1998 to 2020. https://meteo.lcd.lu
Older trends are here!
Attention: in all trend equations (y = a+b*x) the variable x represents the year, with x = 1 for the first year in the trend period.
An Addendum 4 has been added to report on the PM (fine particle) measurements (04Apr20)
Most important conclusions from 1998 (2002, 2008) to 2020:
1. Significant increase in sunshine duration since 2008 by
167
hours*decade^{1}
2. Local temperatures show warming of 0.10°C/y since 2002
(2018 was a strong El Nino year)
3. Diurnal temperature range (DTR) trend since 1998 is positive (= no
anthropogenic warming fingerprint ).
4. The winter trend since 2002 shows a warming of +1.3 °C*decade^{1 }
; this positive trend is also shown by the winter NAO index (+0.6 °C*decade^{1})
5. Since 1998 the ground O3 trend is positive, from
2002 to 2020 the
total thickness of the ozone
layer slightly decreases by 9.3 DU*decade^{1}
6. Local CO2 mixing ratio continues to increase at about 4.8 ppmV per
year; the asymptotic background is close to that measured at Mauna Loa.
7. The trend of the biologically effective yearly UVB dose is
distinctly positive from 2008^{
}
to 2020^{
}
(like solar irradiance and sunshine)^{
}
8. The trend of the UVA dose is distinctly positive from 2008 to 2020 (as
eff. UVB, solar irradiance and sunshine hours)
9. Precipitation (rainfall) shows a sinusoidal pattern of close to 5
years period.
10. Energy content of moist air (enthalpy) shows a positive trend
11. The fine particles PM2.5 and PM10 concentrations are very low, and
are well synchronized with those at Beidweiler.
NO/NO_{x} measurements have been definitively stopped at the end of 2017.
Ground
Ozone [ug/m^{3}] ("bad ozone") Mean +/ stdev
of Mean
+/ stdev (from yearly means)


Total
Ozone Column [DU]
("good ozone") Mean and stdev of the year 2020: 310.1 +/ 31.6 DU minimum : 208.5 (18 Nov) maximum: 426.5 (28 Jan) 183 common day
direct sun readings for 2019 at Uccle and Diekirch:
Calibration
multiplier to apply to the Diekirch DU data
[55] and
[56] 


CO2
mixing ratio in ppmV
Attention: The instrument for measuring CO2 (API Teledyne E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017. The jump from 2017 to 2018 seems implausible high, so a zero bias should be considered possible! Mean and stdev of the year 2020: 440.8 +/ 30.7 ppmV The 19982001 data are too unreliable to be retained
for the trend analysis.
The second picture zooms on the last 3 years, and
gives the readings of Diekirch (DIK), Mauna Loa (MLO)
from 2018 to 2020; and Hohehpeissenberg (HPB) from 2018 to 2019 as 2020 data are not yet available for HPB. Note the very
different elevations! Mauna Loa has no vegetation at all, Diekirch and HPB
similar grass and forests.
Be careful with the Vaisala readings, as the Vaisala GPM343 might not give the same accuracy as the former API! These readings also are given for local atm. pressure and nondried air!
The CO2 data (monthly averages) show the summertime lows, which reflect the impact of variable seasonal photosynthesis (see here). A simple 12 month periodic sinus pattern was also found in 2014 and 2015. Actually, as shown in addendum 3, the CO2 lowering intensity of wind speed seems to be an important modifier of this pattern, possibly masking the effect (or better: the noneffect) of photosynthesis. This happened in 2016 and 2017.
This year 2020 the yearly amplitude of the sinus fit is
8.56 ppmV (a total swing
of ~17.1 ppmV, to be compared to about 12.4 ppmV at the HPB station for 2019 [48]).
See the end of addendum 3 for a picture of CO2 versus windspeed.

.


Air
temperature [°C]
Mean and stdev of the year 2020
(from monthly averages):
1998 to 2020 :
10.68 +/ 0.83 °C The
sensor location has not been moved since 2002! Sensor is a PT100
(see comments in
2015_only.xls); new 420mA
amplifier (with calibration) installed the 4th May 2016. 

Diurnal Temperature Range (DTR) [°C]
DTR = daily max  daily min temperature
Mean DTR at Diekirch:
For 1998 to 2020: all trends are positive, the 24hmin
trend is lower than the 24hmax trend. 


Winter temperatures [°C] Values of winter DJF temperature of the year 2020: Diekirch: 6.04 Findel: 4.37 NOA: 1.27 NAO normalized index [47] The trends show warming winters since 2002 to 2020, with the warming probably caused by the NAO.
Trends from 2002 to 2020: The plot shows the mean temperatures from
December (of previous year) to February. It also shows in
magenta the NAO index for the months Dec to Feb 

Enthalpy of moist air in kJ/kg Mean moist enthalpy of 2020: 32.35 +/ 13.13 kJ/kg See [24] on how the energy content of moist air is
calculated. Several authors, (e.g. Prof. Roger Pielke Sr.) insist that air
temperature is a poor metric for global warming/cooling, and that the energy
content of the moist air and/or the Ocean Heat Content (OHC) are better
metrics. 


Total
Yearly Rainfall [mm]
Values of rainfall (precipitation) of the year 2019: Diekirch: 622.2 mm Findel: 788.7 mm
1998  2020 mean +/ stdev: 682.2 +/ 127.8 mm
The negative trend from 1998 to 2018 seems spectacular:
80 mm/decade, caused by the very high values of 2000 and 2001.
Clearly precipitation shows an oscillation pattern, so linear trends should be taken with precaution (or simply seen as nonsensical).
A good model for the Diekirch data is a
sinus function: the calculation
(LevenbergMarquart algorithm)
suggests for the interval 2002  2020 a 5.21 years period (~62 months, R^{2} =
0.44);
in the model x = 0 corresponds to 2002), with a mean value of 651 mm and an
amplitude of 91 mm; the phase shift of 1.13 rad is close to 1/5 period. All
these values are similar to those of the preceding two years, but note that
the year 2018 is an outlier! The oscillatory rainfall pattern is a good example how foolish it is to apply linear regressions to data when these are harmonic, something the media, activist groups and many politicians often do without much thinking. 


Solar
energy on a horizontal plane
Values of total solar energy of
the year 2020:
1998 to 2020 mean +/ stdev: 1119.5 +/ 51.1 kWh/m2 

Sunshine hours
(meteoLCD values derived from pyranometer data by Olivieri's method)
Values of sunshine hours
of the year 2020:
Trends:
See paper
[23] by F. Massen
comparing 4 different methods to compute sunshine duration from pyranometer
The 2nd graph shows the plots of the four abovementioned stations. It should be noted that meteoLCD (Diekirch) is located in a valley, Findel, Trier and Maastricht airport on top of a plateau. The Findel totals are much higher than those of the other stations, which certainly is also partially caused by the use of the CampbellStokes instrument known to give too high readings. All 4 stations give totals that practically always vary in the same manner (synchronous increase and decrease). The trends of all 4 stations are strongly positive since 2008: those of Diekirch (+167 h/decade), Findel (+188h/decade) and Maastricht (+166 h/decade) are practically the same, whereas TrierPetrisberg shows an astoundingly high trend of +304h/decade), which should be accepted with some precaution!
These strong positive trends probably suffice to explain the warming since 2008:  all correlations between mean temperature are yearly sunshine hours are positive for the 4 stations, and these correlations are all statistically significant at the alpha = 0.95 level: Diekirch 0.71, Findel 0.61, Trier 0.69, Maastrich 0.58  see the last multigraph figure for the tempversussunshine relationship at the 4 stations meteoLCD, Findel, Maastricht and TrierPetrisberg. 


Biologically
eff. UVB dose on a horizontal plane in kWh/m^{2}
Erythemal UVB dose of the year 2020: 0.153 kWh/m^{2} mean +/ stdev: 1999 to 2020: 0.133 +/ 0.009 eff. kWh*m^{2}y^{1} 2002 to 2020: 0.135 +/ 0.008 2008 to 2020: 0.135 +/ 0.009 The trend over
2002  2020 is slightly positive, the trend line from 2008 to 2020
distinctly positive, in concordance with solar irradiance and sunshine
hours. . See
[10]
and [22] (poster finds slight
positive trend in June (+2%) and negative trend in August (1%), no trend
for other months, for period 1991 to 2008. 

UVA
dose on a horizontal plane in kWh/m^{2}
UVA dose of the year 2020: 58.56 KWh/m^{2} (some intermittent problems with internal temperature stabilization of the sensor; the influence seems minimal, so all readings have been kept)
mean +/ stdev: ^{ The 4 independent measures of solar irradiance, sunshine hours, eff.UVB and UVA doses all point to a strong increase since 2008. } 

NO_{x},
NO and NO_{2}
concentration in ug/m^{3
}^{
(End of measurements useable for trends in 2013. Measurements stopped in
2017).} Attention: only
78% of possible measurements available due to sensor downtime!
see [11] which gives ~30% reduction from 1990 to 2005 for the EU15 countries. 

FINE PARTICLES (PM2.5, PM10) in ug/m^{3} ^{(new paragraph)} For most of the year 2020 meteoLCD had 3 different LLS particle sensors working, albeit not all for the full year: The "standard" sensor is the AIRVISUAL from IQAIR
(Switzerland) whose PM2.5 readings are continuously uploaded into the iQAIR
cloud and are accessible
here. The
PM10 and PM2.5 readings are also stored in the internal memory of the sensor
at a rate of 1 measurement every 15 minutes. In this trends report we will only use the Airvisual
data and those of the official BEIDWEILER station (data downloaded from
discomap [60]).
The PM 2.5 readings are acceptable close, but the
Airvisual PM10 readings are far too low. The Airmaster worked only from
April to September. Here the averages for all sensors for these 6 months.
The synchronicity of all sensors is good, and the correlation coefficients
are all significant at the 95% level.
The second graph shows that the Airmaster Pro and PurpleAir PM10 are reasonably close during the April to September period. All PM readings at Diekirch have been corrected for humidity by dividing the raw readings by the growthfactor GF = 1 + (0.25*RH^{2})/(1  RH), where RH is the relative humidity neasured by the internal sensor (RH: 0...1). See [59] 

References:
Addendum
1 2014 update! 
Lindzen & Choi [19] define the nonfeedback
climate sensitivity as ΔT_{0} = G_{0}*ΔF, where G_{0
}= 0.25 Wm^{2} and ΔF is the change in radiative forcing.
A change in solar irradiance of 0.82 kWh*m^{2}y^{1 } (decade 2005 to
2014) corresponds to ΔF
=  820/8760 = 0.09 Wm^{2 }and should yield a cooling of ΔT_{0
}= 0.25*0.09 = 0.02 K (or °C).per year. The meteoLCD measurements
give a cooling of 0.0057 Ky^{1}, about 3 times less. Scafetta [20] defines a climate sensitivity in respect to changes in solar radiation by k_{1s} = ΔT/ΔF and finds k_{1s }= 0.053. Our data for the decade 2005 to 2014 give ΔT/ΔF=  0.0057/(0.09) = 0.06, a value close to that of Scafetta!. Summary for the 2005 to 2014 decade: <\table>
^{ } 
Addendum 2 2018 update!

It makes for an interesting exercise to compare
the influence of mean yearly solar forcing on moist enthalpy and air
temperature for the 17 years period 2002 to 2018.
Both air temperature and moist enthalpy are positively correlated to changes in solar forcing ( = mean solar irradiance). The Pearson correlation between mean solar irradiance and moist enthalpy is 0.73 and is significant at the p = 0.05 level, whereas the correlation between mean solar irradiance and temperature is 0.42 (not significant). A change of 1 Wm^{2} of mean solar irradiance would cause a (big!) average heating of 0.5 °C per decade and a change of 0.9 kJ/kg of moist enthalpy per decade. Possibly taking into account some lag (as for instance 4 months for temperature lagging solar forcing) would change these numbers. Our temperature measurements give a heating of 0.7°C/decade for the same period (Findel shows 0.5°/decade), which is close to the correlation given if the solar irradiance was the unique warming influence!

Addendum
3 
A short
analysis of the seasonal CO2 pattern in 2014. The mean monthly CO2 data show an oscillatory pattern which can be modeled by a 6 month period sine wave. This is not consistent with the commonly admitted explication that the summer lows and winter highs are a fingerprint of changing photosynthesis, which should lead to a single annual sinus wave (as in 2013). The 6 month period is essentially caused by the low Jan, Feb and Dec values, and is replaced by the usual 12 period if these months are omitted. The right figure shows the monthly mean CO2 and monthly mean wind speeds. Clearly low wind goes with high CO2, independent of the seasons (significant correlation R = 0.86 !) The next figure gives the CO2 mixing ratios versus the monthly mean wind speed; the usual exponential model beautifully describes this pattern. The horizontal asymptote of 395.5 ppmV should correspond to the background CO2 level, as shown in [21]. There is some debate about the (global) changes of the seasonal CO2 amplitude, which seems to increase due to global greening [41], agricultural green revolution [43], changing air transcontinental circulation [42] and possibly other unknown factors. Look also at the presentation [44]. Locally it seems that the effects of higher/lower wind speeds and photosynthesis are difficult to untangle. If we restrict our data to those days where the mean wind speed is less than 1, the correlation between CO2 and wind speed is lower (0.76) but still significant. Curiously all the papers studying this seasonal amplitude problem seem to ignore the influence of changing wind speeds.

The same
analysis for 2015 Here again higher wind speeds usually go together with lower CO2 levels (notice the exception on March!), but the monthly mean values do not follow the usual model well.
If we take all 17520 individual measurements, the picture becomes clearer, and we find that our "bumerang" model follows reasonably well the overall pattern. The horizontal asymptote suggest a background CO2 level of about 389 ppmV, which seems a bit low.


The same
analysis for 2016 (wind speed from cup anemometer) The high wind speeds lower the January , February (and December) values which normally should be higher; so the "usual" sinus pattern with a trough during the summer months is mostly absent.
Using all CO2 measurements of the year, we find again our boomerang pattern; the usual model has a better R2 than in 2015, but the asymptotic value of 383 is definitively too low!


CO2
versus wind speed for 2017 (wind speed by cup anemometer): The Mauna Loa average CO2 mixing ratio for 2017 is 406.6, which would suggest that our asymptotic value of 392.3 is too low. If we use only the measurements by the new Vaisala GMP343 sensor, the asymptotic value becomes 395.7.


CO2
versus wind speed for 2018 (wind speed by cup anemometer, 17520 data
points): The Mauna Loa average CO2 mixing ratio for 2018 is 408.5, so our asymptotic value of 406 is quite close. The R^{2} of the model (the goodness of the fit) is also quite acceptable: R^{2} = 0.50. All parameters are significant at the 5% level (alpha = 0.95).


CO2
versus wind speed for 2019 (wind speed by cup anemometer, 17520 data
points): The Mauna Loa average CO2 mixing ratio for 2019 is 411.44, so our asymptotic value of 411 is practically the same! The R^{2} of the model (the goodness of the fit) is also quite acceptable: R^{2} = 0.52. All parameters are significant at the 5% level (alpha = 0.95).


CO2
versus wind speed for 2020 (wind speed by cup anemometer, 17568 data
points):
The Mauna Loa average CO2 mixing ratio for 2020 is
414, so our asymptotic value of 417 is very close, keeping in mind
that CO2 levels increase slightly with latitude!

Addendum 4 2019 
Fine particle measurements at meteoLCD An Airvisual Pro sensor from iQAir has been operational for the full year 2019. Besides temperature, humidity and CO2, this sensor also measures PM2.5 and PM10 concentrations in ug/m3. All data are stored in a cloud managed by iQAir (https://airvisual.com/luxembourg/diekirch/diekirch/meteolcd shows only the PM2.5 concentrations; a private dashboard holds all data). The measuring principle for the PM is LLS (Laser Light Scattering). Ambiant air is sucked into the measuring chamber by a small fan running continuously; there is no drying nor correction for pressure variations. It has been found that the most important correction for this type of sensor is the humidity correction, as above 70% RH condensing water on the aerosol particles inflates the count and the reported mass. At meteoLCD we divide the raw data by a growth factor GF =a+(b*RH**2)/(1RH) with a=1 and b = 0.25 Read the article "One year of fine particle measurements by Airvisual Pro at meteoLCD" on the blog meteolcd.wordpress.com [ref. 59] The next two plot show the PM2.5 readings per day, together with the corresponding values of the official measuring station at Beidweiler, which uses a Horiba sensor.
The correspondence shown on the first plot is excellent; the second plot suggest to divide the Diekirch data by 0.9572, if Beidweiler is considered as the reference. The PM10 readings of the Airvisual Pro are very close (too close!) to it's PM2.5, and as such considerably too low if compared to the Beidweiler readings; we have no explanation for this for the moment.

file: meteolcd_trends.html
History:
09 Mar 2020: Update to include 2019 data finished; not all addendum's updated to 2019
04 Apr 2020: Added Addendum 4 with the fine particle measurements of 2019
20 Jan 2021: Started update to 2020 data
05 Feb 2021: Update to 2020 finished.